Asian Pacific Journal of Cancer Prevention

Asian Pacific Journal of Cancer Prevention (아시아태평양암예방학회)
권호 표지
DOI QR코드

DOI QR Code

Gene Expression Biodosimetry: Quantitative Assessment of Radiation Dose with Total Body Exposure of Rats

  • Saberi, Alihossein (Department of Medical Genetics, Faculty of Medicine, Ahvaz Jundishapour University of Medical Sciences) ;
  • Khodamoradi, Ehsan (Department of Radiology and Nuclear Medicine, Paramedical School, Kermanshah University of Medical Sciences) ;
  • Birgani, Mohammad Javad Tahmasebi (Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapour University of Medical Sciences) ;
  • Makvandi, Manoochehr (Department of Virology, Faculty of Medicine, Ahvaz Jundishapour University of Medical Sciences)
  • Published : 2016.01.11
Download PDF
⟨ Previous Next ⟩

Abstract

Background: Accurate dose assessment and correct identification of irradiated from non-irradiated people are goals of biological dosimetry in radiation accidents. Objectives: Changes in the FDXR and the RAD51 gene expression (GE) levels were here analyzed in response to total body exposure (TBE) to a 6 MV x-ray beam in rats. We determined the accuracy for absolute quantification of GE to predict the dose at 24 hours. Materials and Methods: For this in vivo experimental study, using simple randomized sampling, peripheral blood samples were collected from a total of 20 Wistar rats at 24 hours following exposure of total body to 6 MV X-ray beam energy with doses (0.2, 0.5, 2 and 4 Gy) for TBE in Linac Varian 2100C/D (Varian, USA) in Golestan Hospital, in Ahvaz, Iran. Also, 9 rats was irradiated with a 6MV X-ray beam at doses of 1, 2, 3 Gy in 6MV energy as a validation group. A sham group was also included. After RNA extraction and DNA synthesis, GE changes were measured by the QRT-PCR technique and an absolute quantification strategy by taqman methodology in peripheral blood from rats. ROC analysis was used to distinguish irradiated from non-irradiated samples (qualitative dose assessment) at a dose of 2 Gy. Results: The best fits for mean of responses were polynomial equations with a R2 of 0.98 and 0.90 (for FDXR and RAD51 dose response curves, respectively). Dose response of the FDXR gene produced a better mean dose estimation of irradiated "validation" samples compared to the RAD51 gene at doses of 1, 2 and 3 Gy. FDXR gene expression separated the irradiated rats from controls with a sensitivity, specificity and accuracy of 87.5%, 83.5% and 81.3%, respectively, 24 hours after dose of 2 Gy. These values were significantly (p<0.05) higher than the 75%, 75% and 75%, respectively, obtained using gene expression of RAD51 analysis at a dose of 2 Gy. Conclusions: Collectively, these data suggest that absolute quantification by gel purified quantitative RT-PCR can be used to measure the mRNA copies for GE biodosimetry studies at comparable accuracy to similar methods. In the case of TBE with 6MV energy, FDXR gene expression analysis is more precise than that with RAD51 for quantitative and qualitative dose assessment.

Keywords

References

  1. Badie C, Kabacik S, Balagurunathan Y, et al (2013).Laboratory intercomparison of gene expression assays. Radiation research, 180, 138-48. https://doi.org/10.1667/RR3236.1
  2. Bazan JG, Chang P, Balog R, et al (2014). Novel human radiation exposure biomarker panel applicable for population triage. International Journal of Radiation Oncology* Biology* Physics, 90, 612-9. https://doi.org/10.1016/j.ijrobp.2014.05.046
  3. Boldt S, Knops K, Kriehuber R, et al (2012). A frequency-based gene selection method to identify robust biomarkers for radiation dose prediction. International journal of radiation biology, 88, 267-76. https://doi.org/10.3109/09553002.2012.638358
  4. Filiano AN, Fathallah-Shaykh HM, Fiveash J, et al (2011). Gene expression analysis in radiotherapy patients and C57BL/6 mice as a measure of exposure to ionizing radiation. Radiation research, 176, 49-61. https://doi.org/10.1667/RR2419.1
  5. Forrester HB, Sprung CN (2014). Intragenic controls utilizing radiation-induced alternative transcript regions improves gene expression biodosimetry. Radiation research, 181, 314-23. https://doi.org/10.1667/RR13501.1
  6. Horn S, Barnard S, Rothkamm K (2011). Gamma-H2AXbased dose estimation for whole and partial body radiation exposure. PLoS One, 6, 25113. https://doi.org/10.1371/journal.pone.0025113
  7. Kabacik S, Mackay A, Tamber N, et al (2011). Gene expression following ionising radiation: identification of biomarkers for dose estimation and prediction of individual response. International journal of radiation biology, 87, 115-29. https://doi.org/10.3109/09553002.2010.519424
  8. Khodamoradi E, Saberi A, Tahmasebi M, et al (In Press). Dose response curves of FDXR and RAD51 genes in 6 and 18 MV Beam energies in human peripheral blood lymphocytes. Iran red crescent medical j.
  9. Knops, K., et al. ( 2012) Gene expression in low-and high-doseirradiated human peripheral blood lymphocytes: possible applications for biodosimetry. Radiation research, 178, 304-12. https://doi.org/10.1667/RR2913.1
  10. Manning G, Kabacik S, Finnon P, et al (2013). High and low dose responses of transcriptional biomarkers in ex vivo X-irradiated human blood. International journal of radiation biology, 89, 512-522. https://doi.org/10.3109/09553002.2013.769694
  11. Manning G, Rothkamm K (2013). Deoxyribonucleic acid damage-associated biomarkers of ionising radiation: current status and future relevance for radiology and radiotherapy. The British journal of radiology, 86, 20130173. https://doi.org/10.1259/bjr.20130173
  12. Min X-Y, Zhang X-H, Zhou Q-P, et al (2014). Development of serum zinc as a biological dosimeter in mice. International journal of radiation biology, 90, 909-913. https://doi.org/10.3109/09553002.2014.922718
  13. Paul S, Amundson SA (2008). Development of gene expression signatures for practical radiation biodosimetry. International Journal of Radiation Oncology* Biology* Physics, 71, 1236-44. https://doi.org/10.1016/j.ijrobp.2008.03.043
  14. Paul S, Barker CA, Turner HC, et al (2011). Prediction of in vivo radiation dose status in radiotherapy patients using ex vivo and in vivo gene expression signatures. Radiation research, 175, 257-65. https://doi.org/10.1667/RR2420.1
  15. Romm H, Ainsbury E, Barnard S, et al (2013). Automatic scoring of dicentric chromosomes as a tool in large scale radiation accidents. Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 756, 174-183. https://doi.org/10.1016/j.mrgentox.2013.05.013
  16. Senthamizhchelvan S, Pant G, Rath G, et al (2009). Biodosimetry using micronucleus assay in acute partial body therapeutic irradiation. Physica Medica, 25, 82-87. https://doi.org/10.1016/j.ejmp.2008.05.004
  17. Tsuyama N, Mizuno H, Katafuchi A, et al (2014). Identification of low-dose responsive metabolites in X-irradiated human B lymphoblastoid cells and fibroblasts. Journal of radiation research, rru078.
  18. Tucker JD, Joiner MC, Thomas RA, et al (2014). Accurate gene expression-based biodosimetry using a minimal set of human gene transcripts. International Journal of Radiation Oncology* Biology* Physics, 88, 933-9. https://doi.org/10.1016/j.ijrobp.2013.11.248

Cited by

  1. Ionizing radiation response of primary normal human lens epithelial cells vol.12, pp.7, 2017, https://doi.org/10.1371/journal.pone.0181530